HEALing Communities Study: Data measures for supporting a community-based intervention to reduce opioid overdose deaths.

With increasing rates of opioid use disorder (OUD) and overdose deaths in the US, increased access to medications for OUD (MOUD) is paramount. Rigorous effectiveness evaluations of large-scale implementation initiatives using quasi-experimental designs are needed to inform expansion efforts. To evaluate a US Department of Veterans Affairs (VA) initiative to increase MOUD use in nonaddiction clinics. This quality improvement initiative used interrupted time series design to compare trends in MOUD receipt. Primary care, pain, and mental health clinics in the VA health care system (n = 35) located at 18 intervention facilities and nonintervention comparison clinics (n = 35) were matched on preimplementation MOUD prescribing trends, clinic size, and facility complexity. The cohort of patients with OUD who received care in intervention or comparison clinics in the year after September 1, 2018, were evaluated. The preimplementation period extended from September 1, 2017, through August 31, 2018, and the postimplementation period from September 1, 2018, through August 31, 2019. The multifaceted implementation intervention included education, external facilitation, and quarterly reports. The main outcomes were the proportion of patients receiving MOUD and the number of patients per clinician prescribing MOUD. Segmented logistic regression evaluated monthly proportions of MOUD receipt 1 year before and after initiative launch, adjusting for demographic and clinical covariates. Poisson regression models examined yearly changes in clinician prescribing over the same time frame. Overall, 7488 patients were seen in intervention clinics (mean [SD] age, 53.3 [14.2] years; 6858 [91.6%] male; 1476 [19.7%] Black, 417 [5.6%] Hispanic; 5162 [68.9%] White; 239 [3.2%] other race [including American Indian or Alaska Native, Asian, Native Hawaiian or other Pacific Islander, and multiple races]; and 194 [2.6%] unknown) and 7558 in comparison clinics (mean [SD] age, 53.4 [14.0] years; 6943 [91.9%] male; 1463 [19.4%] Black; 405 [5.4%] Hispanic; 5196 [68.9%] White; 244 [3.2%] other race; 250 [3.3%] unknown). During the preimplementation year, the proportion of patients receiving MOUD in intervention clinics increased monthly by 5.0% (adjusted odds ratio [AOR], 1.05; 95% CI, 1.03-1.07). Accounting for this preimplementation trend, the proportion of patients receiving MOUD increased monthly by an additional 2.3% (AOR, 1.02; 95% CI, 1.00-1.04) during the implementation year. Comparison clinics increased by 2.6% monthly before implementation (AOR, 1.03; 95% CI, 1.01-1.04), with no changes detected after implementation. Although preimplementation-year trends in monthly MOUD receipt were similar in intervention and comparison clinics, greater increases were seen in intervention clinics after implementation (AOR, 1.04; 95% CI, 1.01-1.08). Patients treated with MOUD per clinician in intervention clinics saw greater increases from before to after implementation compared with comparison clinics (incidence rate ratio, 1.50; 95% CI, 1.28-1.77). A multifaceted implementation initiative in nonaddiction clinics was associated with increased MOUD prescribing. Findings suggest that engagement of clinicians in general clinical settings may increase MOUD access.

ObjectiveDemonstrate the use of timely, actionable data from a data visualization tool, the California Opioid Overdose Surveillance Dashboard, which integrates statewide, geographic- and demographic-specific data, by describing the changes in opioid overdose deaths in California.IntroductionCalifornia continues to face a serious public health crisis with the opioid epidemic having substantial health and economic impacts. The epidemic is dynamic and rapidly changing, involving both prescription opioids influenced by prescribing and dispensing patterns as well as illicit opioids influenced by the availability of heroin and recently, the increased availability of fentanyl. The complexity of the issue necessitates data-informed actions through multi-sector, strategic collaboration at both the state and local levels to address the problem comprehensively. With nearly 2,000 opioid overdose deaths per year and wide variation of overdose rates across counties and demographic groups, there is a need for integrated, timely, actionable data for use by state policy makers, local opioid safety coalitions, media, community stakeholders, and the public to monitor and combat this dynamic epidemic at the state and local level. Using fatality data from the California Opioid Overdose Surveillance Dashboard1, the opioid overdose epidemic is described along with the differential geographic and demographic impacts.MethodsAs part of California Department of Public Health’s Prevention for States grant funded by the Centers for Disease Control and Prevention, the California Opioid Overdose Surveillance Dashboard was developed as a data tool to provide enhanced visualization and integration of non-fatal and fatal opioid-involved overdose data and opioid prescription data. The dashboard was built on an open source RStudio server using Shiny, an R package that provides a framework for building web applications. Data incorporated on the dashboard include emergency department visits, hospitalizations, fatalities, and prescriptions related to opioid overdoses among California residents, presented in raw counts, crude rates, and age-adjusted rates at the state, county, and zip code levels, as well as by sex, age, and race/ethnicity. Overdose deaths are identified using ICD-10 (International Classification of Diseases, 10th Revision) codes X40-X44, X60-X64, X85, Y10-Y14, and T40.0-T40.6, recorded in the underlying cause of death and multiple cause of death fields on death certificates. Fentanyl overdose deaths are identified using a text search on contributing cause of death fields on death certificates. Using data from the California Opioid Overdose Surveillance Dashboard, we present one perspective of the epidemic by using 2017 death data to describe the changing trend and geographic and demographic variation of prescription drug, heroin, and fentanyl overdose deaths.ResultsOverall trends from 2011-2017 show that deaths due to opioid overdoses have increased. Prescription drug overdose death rates have slightly decreased by 6% from 3.93/100,000 in 2011 to 3.7/100,000 in 2017. Heroin overdose death rates have increased by 89% from 0.90/100,000 in 2011 to 1.70/100,000 in 2017. Fentanyl overdose death rates have increased by 320% from 0.25/100,000 in 2011 to 1.05/100,000 in 2017. The highest rates of prescription opioid overdose deaths are primarily concentrated in northern rural counties, while the highest rates of heroin and fentanyl overdose deaths are more dispersed throughout the state with many coastal counties showing higher rates of overdose deaths (Figure 1). Prescription opioid overdose deaths are concentrated among older ages showing highest rates among 55 to 59 year olds (8.27/100,000). In contrast, heroin and fentanyl overdose death rates are concentrated among younger ages with the highest rates seen among 25 to 29 year olds, 4.54/100,000 and 2.78/100,000, respectively (Figure 2). Males died from prescription opioid, heroin, and fentanyl overdoses at significantly higher rates than females. Prescription opioid and fentanyl overdose death rates (11.5/100,000 and 4.80/100,000, respectively) are significantly higher among Native Americans compared to other races/ethnicities (Table 1). Non-Hispanic whites had significantly higher prescription opioid and heroin overdose death rates (6.90/100,000 and 2.96/100,000, respectively) compared to non-Hispanic black, Hispanic, and Asian residents of California.ConclusionsFatality data from 2017 show the characteristics of the opioid overdose epidemic in California are changing. While still high, overdose deaths from prescription opioids, seen primarily in older age groups and northern rural California, are slightly declining. Concurrently, we are seeing sharp rises in heroin and fentanyl overdose death rates among younger adults throughout the state. Regardless of any change in trend, there remain clear disparities in overdose death rates by race/ethnicity; with Native Americans having the highest rates for both prescription and illicit opioids, and non-Hispanic whites have higher rates of prescription opioid and heroin overdose deaths.Given the varying demographic and geographic impacts based on the type of opioid, as demonstrated with the use of death data, there needs to be targeted data-informed interventions to address and prevent prescription and illicit opioid overdoses. Death data is just one perspective on the epidemic, other data sources (emergency department visits, hospitalizations, and prescriptions) are needed complete the picture to truly provide a robust data-informed approach. The California Opioid Overdose Surveillance dashboard integrates these multiple data sources and serves as a valuable tool in providing specific and timely data to inform approaches and interventions at the state and local level in continuing to fight California’s opioid overdose epidemic. The enhanced visualization, geographic- and demographic-specific data, and increasingly timely data allow for state and local policy makers, local opioid safety coalitions, and community stakeholders to track the dynamics and impact of the epidemic and to identify those who are most vulnerable and differentially impacted.References1 California Opioid Overdose Surveillance Dashboard https://discovery.dev.cdph.ca.gov/CDIC/ODdash/

Effects of the Communities That HEAL intervention on initiation, retention, and linkage to medications for opioid use disorder (MOUD): A cluster randomized wait-list controlled trial.

BackgroundDespite significant efforts to address the opioid overdose crisis, Black people who use drugs (PWUD) face unabating, disproportionate increases in opioid overdose death (OOD) rates. These inequities persist in treatment admissions, utilization of medication for opioid use disorder, and treatment retention. Research has linked neighborhood disinvestment — a process of urban decline driven by policy-related changes in neighborhood demand and desirability leading to decreased population, physical and economic erosion, and poorer quality of life for residents — to increased rates of OOD. However, given recent increases in OOD inequities, more research is needed to determine the specific aspects of neighborhood disinvestment that drive OOD risk among Black PWUD.MethodsThe current qualitative study utilized a community-engaged research approach to conduct focus groups with stakeholders providing support to PWUD in Black neighborhoods in order to identify the facets of neighborhood disinvestment that contribute and mitigate increases of OOD among Black PWUD in St. Louis, Missouri.ResultsThe resulting thematic analysis identified four themes linking neighborhood disinvestment to increased rates of OOD among Black PWUD: (1) a lack of access to congruent treatment and services, (2) intergenerational and socioemotional lack of mobility, (3) lack of financial accountability and investment from local leadership and government, and (4) the loss of collective community responsibility and engagement. A fifth theme brought attention to a culturally-grounded strategy being used to reduce these rates: (5) building engagement and community cohesion through grassroots efforts and street outreach.ConclusionsFindings provide key implications for policy and practice, including the importance of adopting a community-based research framework, offering financial management training for Black-led organizations, and harnessing community champions to implement culturally-tailored interventions aimed at reducing stigma and raising critical consciousness. Future work should aim to identify more effective community-driven solutions to address OOD in Black neighborhoods.

To quantify the association between opioid agonist treatment (OAT) and overdose death by age group; test the hypothesis that across different age groups, opioid overdose mortality is lowest during OAT with buprenorphine compared with time out of treatment or OAT with methadone; and test associations between OAT and opioid overdose mortality in the presence of chronic circulatory, respiratory, liver and kidney diseases. Retrospective observational cohort study using linked administrative data. New South Wales, Australia. A total of 37 764 people prescribed OAT, 1 August 2002 and 31 December 2017. OAT exposure, opioid overdose mortality and key confounders were measured using linked population data sets on OAT entry and exit, hospitalization, mental health care, incarceration and mortality. ICD-10 codes were used to define opioid overdose mortality and chronic disease groups of interest. Relative to time out of treatment, time in OAT was associated with a lower risk of opioid overdose death across all age groups and chronic diseases. Among people aged 50 years and older, there was weak evidence that buprenorphine may be associated with greater protection against opioid overdose death than methadone [generalized estimating equation (GEE) adjusted incident rate ratio (aIRR) = 0.47; 95% confidence interval (CI) = 0.21, 1.02; marginal structural models (MSM) aIRR = 0.49; 95% CI = 0.17, 1.41]. Buprenorphine was associated with greater protection against overdose death than methadone for clients with circulatory (MSM aIRR = 0.27; 95% CI = 0.11, 0.67) or respiratory (MSM aIRR = 0.26; 95% CI = 0.07, 0.94) diseases, but not liver (MSM aIRR = 0.59; 95% CI = 0.14, 2.43) or kidney (MSM aIRR = 1.16; 95% CI = 0.31, 4.36) diseases. Opioid agonist treatment (OAT) appears to reduce mortality risk in people with opioid use disorder who are older or who have physical comorbidities. Opioid overdose mortality during OAT with buprenorphine appears to be lower and reduced in clients with circulatory and respiratory diseases compared with OAT with methadone.

Patients seeking treatment for opioid use disorder face more challenges during pandemic

In 2020, the ongoing US opioid overdose crisis collided with the emerging COVID-19 pandemic. Opioid overdose deaths (OODs) rose an unprecedented 38%, due to a combination of COVID-19 disrupting services essential to people who use drugs, continued increases in fentanyls in the illicit drug supply, and other factors. How much did these factors contribute to increased OODs? We used a validated simulation model of the opioid overdose crisis, SOURCE, to estimate excess OODs in 2020 and the distribution of that excess attributable to various factors. Factors affecting OODs that could have been disrupted by COVID-19, and for which data were available, included opioid prescribing, naloxone distribution, and receipt of medications for opioid use disorder. We also accounted for fentanyls’ presence in the heroin supply. We estimated a total of 18,276 potential excess OODs, including 1,792 lives saved due to increases in buprenorphine receipt and naloxone distribution and decreases in opioid prescribing. Critically, growth in fentanyls drove 43% (7,879) of the excess OODs. A further 8% is attributable to first-ever declines in methadone maintenance treatment and extended-released injectable naltrexone treatment, most likely due to COVID-19-related disruptions. In all, 49% of potential excess OODs remain unexplained, at least some of which are likely due to additional COVID-19-related disruptions. While the confluence of various COVID-19-related factors could have been responsible for more than half of excess OODs, fentanyls continued to play a singular role in excess OODs, highlighting the urgency of mitigating their effects on overdoses.

SummaryBackgroundThe syndemic of injection drug use and serious injection-related infections is leading to increasing mortality in the USA. Although outpatient treatment with medications for opioid use disorder reduces overdose risk and recurrent infections, hospitalisation remains common. We evaluated the clinical impact, costs, and cost-effectiveness of hospital-based strategies to address the US opioid epidemic.MethodsWe developed a microsimulation model to compare the cost-effectiveness of: standard hospital care—detoxification for opioids, no addiction consult service (status quo); expanded inpatient prescribing of medications for opioid use disorder, including bridge prescriptions (ie, medication until they can see an outpatient provider) when possible (medications for opioid use disorder with bridge); implementation of addiction consult services within the hospital (addiction consult services alone); and a combined medication for opioid use disorder with addiction consult services strategy (combined). We used clinical trials and observational cohorts to inform model inputs. Outcomes were life-years, discounted costs, incremental cost-effectiveness ratios, hospitalisations, and deaths. We did deterministic sensitivity analyses on key model inputs related to costs and sequelae of drug use and probabilistic sensitivity analysis to further address uncertainty.FindingsAmong people who inject opioids in the USA, we estimated that expanding medications for opioid use disorder with bridge prescriptions would reduce hospitalisations and overdose deaths by 3·2% and 3·6%, respectively, and the combination of expanded medications with opioid use disorder along with addiction consult sevices would reduce hospitalisations and overdoses by 5·2% and 6·6%, respectively, compared with the status quo. Mean lifetime costs ranged from US$731 400 (95% credible interval 447 911–859 189 for the medications for opioid use disorder strategy) to $741 200 (470 930–868 551 for the combined strategy) per person. Assuming a willingness-to-pay threshold of $100 000 per life-year gained, medications for opioid use disorder with bridge and combined strategies were cost-effective ($7600 and $14 300, respectively). A scenario that assumed ideal access to harm reduction services came to the same conclusions as the base case and our results were robust in deterministic and probabilistic sensitivity analyses.InterpretationThe combined interventions of expanding hospital-based prescribing of medications for opioid use disorder and implementing addiction consult services could improve life expectancy, be cost-effective, and could be the basis for a comprehensive hospital-based strategy for addressing the opioid epidemic in the USA and countries with similar opioid epidemics.FundingNational Institute on Drug Abuse and National Institute of Allergy and Infectious Diseases.

BackgroundAn emerging public health threat of methamphetamine/opioid co-use is occurring in North America, including increases in overdoses related to concomitant methamphetamine/opioid use. This presents a potential risk to established treatments for opioid use disorder (i.e., medications for opioid use disorder [MOUD]). To date, few studies have examined the impact of methamphetamine use on MOUD-related outcomes, and no studies have synthesized data on MOUD retention.MethodsA scoping review was undertaken to examine the impact of methamphetamine use on MOUD retention. All original published research articles were searched in Embase, MEDLINE, PsychINFO, CINAHL, Scopus, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and Cochrane Protocols, and Google scholar databases. Data were extracted into a standardized data extraction chart. Findings were presented narratively.ResultsAll eight included studies demonstrated an increased likelihood of treatment discontinuation or dropout among patients enrolled in MOUD who used methamphetamine. The frequency of methamphetamine use was also associated with MOUD dropout, in that those who used methamphetamine more often were more likely to discontinue MOUD. The definitions and measurements of MOUD retention varied considerably, as did the magnitude of effect size.ConclusionsResults indicate that methamphetamine use has an undesirable impact on MOUD retention and results in an increased risk of treatment discontinuation or dropout. Strategies to identify concurrent methamphetamine use among individuals engaging in MOUD and educate them on the increased risk for dropout should be undertaken. Further research is needed to understand how MOUD retention among patients with concomitant opioid and methamphetamine use can be improved.

A large body of research demonstrates the safety and efficacy of medication for opioid use disorder to reduce opioid misuse and related harms. However, stigma is a substantial barrier to broader use of these medications. This endpage reviews the impact of stigma toward medication for opioid use disorder on treatment access, uptake, retention, and outcomes. I then suggests areas for future research and social change. As one of the primary service providers for people with opioid use disorder, social workers should be at the forefront of efforts to destigmatize medication treatments. Social workers are well positioned to address the consequences of stigma toward medication for opioid use disorder through research, education, practice, and systems change. VIDEO ABSTRACT SCRIPT Methadone, buprenorphine, and extended-release naltrexone, the three FDA-approved medications for opioid use disorder, are among the most powerful tools we have in our fight against the rising drug overdose crisis. A large body of research demonstrates the safety and efficacy of these medications to reduce opioid cravings, improve treatment outcomes, and prevent overdose death. However, stigma is a major barrier to their use. Despite a strong evidence base, medications for opioid use disorder are commonly viewed as ineffective and illegitimate treatments that simply ‘substitute one drug for another.’ Stigma is encoded in stringent laws and regulations which limit access to medication. For people with opioid use disorder, stigma can influence beliefs about medications and affect the likelihood that one will initiate and maintain treatment. Stigma also erodes self-esteem and social support, two important predictors for recovery outcomes. In this way, stigma toward medications for opioid use disorder has become a pressing social justice issue. As one of the primary service providers for people with opioid use disorder, social workers should be at the forefront of efforts to destigmatize medication treatments. This endpage is a call to action for social workers to address stigma toward medication treatments through research, education, practice, and systems change.

Opioid Use Disorder (OUD) and opioid overdose (OD) impose huge social and economic burdens on society and health care systems. Research suggests that Medication for Opioid Use Disorder (MOUD) is effective in the treatment of OUD. We use machine learning to investigate the association between patient's adherence to prescribed MOUD along with other risk factors in patients diagnosed with OUD and potential OD following the treatment. We used longitudinal Medicaid claims for two selected US states to subset a total of 26,685 patients with OUD diagnosis and appropriate Medicaid coverage between 2015 and 2018. We considered patient age, sex, region level socio-economic data, past comorbidities, MOUD prescription type and other selected prescribed medications along with the Proportion of Days Covered (PDC) as a proxy for adherence to MOUD as predictive variables for our model, and overdose events as the dependent variable. We applied four different machine learning classifiers and compared their performance, focusing on the importance and effect of PDC as a variable. We also calculated results based on risk stratification, where our models separate high risk individuals from low risk, to assess usefulness in clinical decision-making. Among the selected classifiers, the XGBoost classifier has the highest AUC (0.77) closely followed by the Logistic Regression (LR). The LR has the best stratification result: patients in the top 10% of risk scores account for 35.37% of overdose events over the next 12 month observation period. PDC score calculated over the treatment window is one of the most important features, with better PDC lowering risk of OD, as expected. In terms of risk stratification results, of the 35.37% of overdose events that the predictive model could detect within the top 10% of risk scores, 72.3% of these cases were non-adherent in terms of their medication (PDC <0.8). Targeting the top 10% outcome of the predictive model could decrease the total number of OD events by 10.4%. The best performing models allow identification of, and focus on, those at high risk of opioid overdose. With MOUD being included for the first time as a factor of interest, and being identified as a significant factor, outreach activities related to MOUD can be targeted at those at highest risk.

An interrupted time series analysis of fentanyl, naloxone, and opioid-involved deaths in five counties in Eastern Missouri

BackgroundHospitalizations for severe injection related infections (SIRI) amongst persons who inject drugs (PWID) are often complicated by patient directed discharge (PDD), incomplete antibiotic treatment and 90-day readmission. Despite a strong association with improved clinical outcomes, medications for opioid use disorder (MOUD) remain significantly underutilized in this patient population. Beginning in January of 2022, our institution revised several key policies that had limited inpatient use of MOUD while also implementing a new order set to facilitate MOUD inductions. This study seeks to quantify the clinical impact of these interventions in PWID admitted with SIRI.MethodsWe first performed a retrospective analysis of all PWID hospitalized with SIRI between January 2018 and December 2020 to establish baseline rates of PDD, antibiotic completion and 90-day readmission in patients who received MOUD compared to those who did not. Subsequent analyses will utilize a quasi-experimental design to compare the same clinical outcomes in patients admitted before and after institutional guideline revision and MOUD order set implementation.ResultsA total of 97 patients were included in our baseline assessment, of which 21 (21.6%) received MOUD while hospitalized. Of patients receiving MOUD, 11 (52.4%) were new inductions. Use of MOUD was associated with significantly lower rates of PDD (5 [23.8%] MOUD patients vs 40 [53.3%] no MOUD patients; OR, 0.273 [95% CI, 0.091-0.823]) and significantly higher rates of antibiotic completion (17 [81.0%] MOUD patients vs 36 [48.0%] no MOUD patients; OR, 4.6 [95% CI, 1.41-14.98]).ConclusionOur baseline data agrees with existing literature in finding MOUD to be an effective way of reducing PDD and increasing antibiotic completion in PWID admitted with SIRI. However, overall MOUD utilization was low, particularly after accounting for the effect of individuals prescribed MOUD prior to admission. Forthcoming data will assess the impact of institutional guideline revision and MOUD order set implementation on PDD, antibiotic completion and hospital readmission in PWID admitted with SIRI.DisclosuresAll Authors: No reported disclosures.

Background Problem-solving courts use an interdisciplinary approach with treatment mandates, hearings, and monitoring to rehabilitate individuals arrested for drug-related crimes or lost custody of children due to drug use. Medications for opioid use disorder (MOUD) are the standard of care for treating opioid use disorder (OUD), but few problem-solving court clients with OUD are referred to MOUD. Previous studies found court staff often harbor misconceptions about MOUD and could benefit from MOUD education. Tailoring education to the intended audience is an educational best practice. We sought to identify content and style preferences for two MOUD education videos: 1) an introduction to MOUD and, 2) MOUD myths/misconceptions. Methods We recruited 40 Florida problem-solving court staff. Using semi-structured interviews, invited document/script edits, and qualitative surveys, we collected data at each of four video development stages. We used template analysis for qualitative data. Results Court staff desired the following content: OUD as a chronic brain condition and MOUD as an effective response; MOUD risks and benefits; how MOUD is accessed; and the appropriate role of court staff with MOUD decisions. Style preferences were: no juvenile/cutesy animation; relatable characters/environments; simple concept illustration; individualizing the learning experience; and combinations of scientific animated videos and successful stakeholder interviews. Discussion Our findings reinforce the importance of tailoring MOUD education to the audience. Court staff’s wish for education about their appropriate role with MOUD reflects their unique position making treatment referrals. Court staff’s desire for stakeholder recordings of success stories mirrors the importance of opinion leaders in other dissemination studies.

To describe patterns of medication for opioid use disorder (MOUD) during pregnancies in the opioid use disorder (OUD) cohort of MAT-LINK, a sentinel surveillance network of pregnancies at US clinical sites. Seven clinical sites providing care for pregnant people with OUD collected electronic health record data. Pregnancies were included in this analysis if (1) the pregnancy outcome occurred between January 2014 and August 2021, (2) the person had OUD, and (3) there was any electronic health record-documented MOUD during pregnancy. Analyses describing MOUD type, demographic characteristics, and timing during pregnancy were performed. Among 3911 pregnancies with any documented MOUD, more than 90% of pregnancies with methadone were to publicly insured people, which was greater than percentages for pregnancies with other MOUD. Buprenorphine with naloxone and naltrexone were two MOUD types that were increasingly common among pregnant people in recent years. In most pregnancies, prenatal care and MOUD were first documented in the same trimester. During the first, second, and third trimesters, there were 37%, 61%, and 91% of pregnancies with MOUD, respectively. Approximately 87% (n = 3412) had only 1 documented MOUD type, versus 2 or 3 types. However, discontinuity in MOUD across trimesters was still observed. In MAT-LINK's OUD cohort, the overall frequency of MOUD improved over the course of pregnancy. Contextual factors, such as insurance status and year of pregnancy outcome, might influence MOUD type. Prenatal care and MOUD might be facilitators for one another; however, there are still opportunities to improve early linkage and continuous access to both prenatal care and MOUD during pregnancy.